Increased consumption of legumes improves arterial stiffness in peripheral vascular disease independent of blood pressure, weight and serum cholesterol
Bibliographic record
Abstract
Background Eating fibre‐rich, low glycemic legumes may benefit cardiovascular health by reducing plasma lipid levels. We therefore undertook a clinical study to address the hypothesis that increased legume consumption would improve arterial stiffness. Methods Individuals with peripheral arterial disease were given ½ cup mixed legumes daily for 8 wks. Results No changes in glycated hemoglobin, blood pressure, body weight, triglycerides or HDL cholesterol were detected relative to baseline values, but there were significant decreases in total and LDL cholesterol. There was an improvement in ankle‐brachial index (ABI) and a corresponding decrease in arterial stiffness (by pulse wave analysis), but serum markers of endothelial dysfunction or inflammation remained constant. Interestingly, the improvements in ABI and cholesterol were not correlated. Conclusions These data indicate that a legume‐rich diet can elicit major improvements in arterial function in the absence of changes in either body mass or blood pressure. Furthermore, the lack of correlation with serum cholesterol levels suggests improved vascular function was not a consequence of the high fibre and low glycemic index properties of the legumes. Finally, the absence of changes in circulating markers of endothelial dysfunction suggests the positive effects on arterial stiffness are mediated by another mechanism. Funding from Pulse Canada.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".